Legal Leaders Exchange - Special edition

The power of data analytics and monetization: Driving growth and innovation

This episode is a special edition on data monetization from our Wolters Kluwer colleagues, featuring Nancy McKinstry, CEO and Chair of Wolters Kluwer, and Barbara Wixom, Principal Research Scientist at the MIT Center for Information Systems Research. Wolters Kluwer’s Erin Aksoy hosts as the speakers discuss:

• The opportunity that companies have to monetize their data and the “improve, wrap, sell” model of data monetization
• Making good decisions about exactly how to realize the greatest value from data
• Examples of how companies, including Wolters Kluwer, have transformed their data usage to improve performance and results at various organizational levels
• The importance of artificial intelligence in realizing value from data and the capabilities that are necessary to do so successfully
• How to approach data monetization in a way that remains focused on customer needs 

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Transcript

Erin Aksoy

Good afternoon, I'm pleased to welcome Barbara Wixom, Principal Research Scientist at MIT Center for Information Systems Research. Also a very warm welcome to Wolters Kluwer’s own CEO Nancy McKinstry. Nancy, Barbara, it's great to have both of you here to speak with us on the value of data analytics and data monetization. My name is Erin Aksoy. I'm part of the data analytics team and I'm moderating today. I want to start by congratulating both of you on your milestones, Nancy, 20 years at Wolters Kluwer as CEO personally guiding us through tremendous growth. This week, also launches Barb's new book, Data is Everybody's Business at MIT Press.

Nancy McKinstry

Great. First of all, great news, Barb, and congratulations on your new book, it's really a very relevant topic. And we're thrilled that you chose us as the place to launch the book. Data monetization is not only, I think, a fascinating topic, but very relevant to Wolters Kluwer given our strategy around driving towards expert solutions, which is really now becoming the core of what we do. So what I'd like to start with is just a couple questions on how can we scale the value created from data that comes from our customer applications and from the business? And how can our customers benefit from the data analytics to better serve society and better serve them as professionals?

Barbara Wixom

I think for one an appreciation of the broadness of what data monetization entails, meaning – you can not only sell datasets in order to create returns from data. In fact, that's a very small part of what we mean by data monetization. But actually, we can improve, wrap or sell our data for value creation for our customers, as well as for organizations. And so we can use our data analytics and improve work, make work better, cheaper, faster, which drives quite a bit of financial returns, we can use our data analytics, and we can wrap new experiences and features that delight our customers and create lift in our products and drive sales growth and such. Or we can create new revenue streams, something that it seems like is going on in your organization in terms of creating informational solutions that solve hard problems in the marketplace, that are a value. And so there's so much that can be done. And so creating pervasive activity, and engagement and monetization, then you're able to really personalize what's going on based on the needs of the organization.

Nancy McKinstry

So one of the concepts in your book is improve, wrap sell, and maybe if you could highlight just an example or two on how do organizations go about doing that? I think we do elements of improve, wrap sell, but not probably in a way that is systematic.

Barbara Wixom

Yeah. Well, I think for decades, most organizations have focused on improving using data analytics, automating reengineering tasks, trying to introduce data analytics in order to predict, inform, so we can take the manual effort out of tasks, whether it be compliance reviews, or submissions, of some kind of regulatory forum or set, right. And there's big money in that. In fact, if you look at overall returns that companies make from data monetization, 51% of the returns, is in improving. So that's really special. And there's still a lot of low hanging fruit in most organizations. But I think recently, what has been introduced is this idea of data wrapping, where it's, we have a pre-existing product. And that could be a banking service, where we're going to add maybe some kind of data analytics feature like an ESG score. So that I understand as a banking client, what the implications of ESG is in my investments, for instance, right. But then you can also wrap diapers. You can have a diaper where there's a mobile app that alerts a parent, as to whether a diaper needs to be changed, right. And that, of course, adds value in a very different way, sometimes even more important ways, right? And so that value lift, and with data wrapping, what's important is just staying focused on returns, what's the actual value being created and realized in that exchange, so that we're not just creating digital features, for instance, just to create them, you know, that we're purposeful about what we're doing.

Nancy McKinstry

And as you think about your next book on this subject, I'd love to get your thoughts on LLMs. We've been using AI for quite some time. 50% of our digital revenues already use some form of AI but of course, the hype around LLMs is quite significant. And I would love to see how that's factoring into your work and the clients that you are working with.

Barbara Wixom

So what the book is intended to do is to help organizations set foundational capabilities, foundational strategies like improve or upsell. We have to do that first. And then in terms of capability, when you have advanced level capabilities, then you can engage in some of the sexier types of activities that are in the marketplace, for instance, AI. So in fact, to be successful and sustainable in your AI efforts, you have to be at an advanced level of capability to do so. So that's kind of book part two is, is once you've laid the foundations in book one, then that will let us strive for and achieve success in AI, for instance, another edgier topic that's more complicated would be data sharing. So data sharing between a provider and a customer, data sharing even within a large organization, as you're reusing and recombining data assets. Sustainability is really hot, we're seeing a lot of activity in that. So those would be the current research topics that we're going after.

Nancy McKinstry

Okay, very interesting, because I think that what we hear from our customers is, there's clearly a move towards wanting more and more insights. And of course, a lot of them have tremendous pressure on just reporting, which requires data analytics, etc. But I also think, on the flip side, this concern about a black box, right, tell me how you got there.

Barbara Wixom

Well, in Book Two, we're gonna add a capability. So, there are five capabilities you need for monetization. But for AI, you actually need a sixth capability, we call it AI explanation. And that's your trust building capability. And we actually have dimensions that need to be in place like bias, remediation, decision tracing, those types of skills have to happen for what you're talking about, for that trust to occur and to be successful.

Nancy McKinstry

And do you believe that all the industries that you've observed in your research, can build a strategy around monetization?

Barbara Wixom

Oh, absolutely. Absolutely. And in the book, we talk about four archetypal strategies that you can achieve. So, for some organizations using data analytics for operational optimization, that is sufficient and appropriate for their business model for their strategic intent. Whereas then you have one model or one aspiration, which is what we call future-ready, which is when you're improving, you're wrapping you're selling, you're acting like a digital organization. And that's appropriate for some, as well, not all. It just depends on again, what strategically you're trying to accomplish as an organization.

Nancy McKinstry

What is your recommendation around how organizations get started? We can always figure out ways we need to improve, but a successful model so that we get that rapid education and rapid innovation and then deployed as quickly as we can. But how have you seen other organizations do that?

Barbara Wixom

It's always about the value, seeing the value, having evidence of value, and then people fall in line. That's really what matters. And so anything that can result in some type of evidence that this is successful, this is beneficial, and then the rest comes. And that's actually why I like to talk about data monetization, as opposed to data. You lead with value, the rest happens. That being said, what I have seen consistently in 29 years of organizational study, is the importance of leadership, and of clarity of vision to direct activity in the appropriate ways.

Erin Aksoy

You guys have talked about some amazing things I've really appreciated hearing, especially about customer understanding, and really rooting ourselves in the customer problem, customer pain points. And if you focus on that, the monetization can follow if you have the proper structure, Barb.

Barbara Wixom

We do a lot of research at our center. And my colleagues do work on board savvy, leadership savvy at the highest levels. How do you keep up because it feels like technology and data analytics would be a part of a lot of the decisions you're making and a lot of the leadership directives that you're giving.

Nancy McKinstry

Yeah, so we've created this centralized group called DXG, which stands for Digital Experience Group, and they play two roles. One is to curate tools. So we don't do what I call fundamental R&D, right and fundamental technology, but we license tools. And so they curate that they are expert in going out and looking at different tools in various different areas of technology, bringing it back, and they also then build applications. And so it's in that reuse that's where we get real scale and real benefit. And so what we try and do is they are the experts and the leaders are used to working with that those experts, but we also try and educate people. So, you might imagine, LLM 's are the focus for a lot of people. So, we do a lot of education among leaders so that they have some, as you were mentioning, you have to have a foundation to begin to understand the technology well enough. So you can identify use cases and talk to your customers about it.

Barbara Wixom

What we talk a lot about in the book is this importance of practicing. Because by practicing, then there's a lot of knowledge-building skill, building, really understanding what's going on, so that you can absorb and then make decisions differently, as you move forward, that are more informed.

Nancy McKinstry

And as you see the students that are now deciding that this is their field of study, what do you find most excites them about that? Because you know, part of what we have to do is, of course, be able to continue to attract people with those talents and as we begin to build out more and more applications for monetization.

Barbara Wixom

The young people who I work with, and who I observe, they want to make a difference in the world, they want to do stuff, and they want to do stuff that matters. And what's exciting in data analytics is bringing data analytic skills to projects, to subject matter to domains, where there's just an obvious impact. So it's not about just working with data analytics. It's truly how are we creating outcomes, and not just financial ones that I would argue are very important in my book, but also now sustainability types of outcomes, for instance. And so getting opportunities to really act and see the results of your labor, and you're making this world a better place. That's highly appealing. And fortunately, there's a lot of opportunities to do that these days.

Nancy McKinstry

And how do you see the – always the balancing act, I think within society between technological advancement, is regulation. And so, if you look at, particularly in the area of data, you know, there's been, as you are well versed, a lot of privacy rules and regulations that have merged. I think there's still a lot of discussion with LLM 's and what does that mean? And how is it all going to work? How do you incorporate that very changing environment, which not only changes as technology advances, but also globally, right? There's different rules, and different philosophies about data and its reuse, its sharing, its monetization. That's such a complex topic. So how do you get your arms around it as you think about researching here?

Barbara Wixom

You know, it's interesting, so much of data seems like it has to be complicated. It really doesn't, we really can simplify a lot of what's going on, including what I call acceptable data use. I purposely don't use words like data governance, because I feel like it's too narrow of a scope. Governance, to me, or to a lot of people is regulation and law. Whereas when we're talking about data, we also have to incorporate oversight for ethics, as well as values of both organization and organization stakeholders. And so it comes down to what capabilities you have in place. You have to build an acceptable data use capability, that's one of the five core capabilities to even be able to engage in data analytics, that's what we see in the research. And the better that capability, then the more successful you're going to be in whatever pursuits you have. And then that AI explanation capability, that's also important, because that gets at that trust component and making sure that not just is the data managed and used properly, but also the models are acting in the way that they need to act and all of that. So, if you focus on capability building in the right way, then it actually solves a lot of problems. And I haven't seen a problem yet that can't be solved, when there's a reason to do so.

For instance, one of our cases was a collection of patient health data, which as you know is some of the most personal protected type of data you can get. However, by taking the time to, in a year, create a data asset that used synthetic data, that was de-identified, where a lot of attention was invested to make sure that this new data set had the same predictive power as the previous. And all of a sudden, now you have a data asset that actually can do quite a bit with no harm, you know, potentially if there's oversight, so there's ways to get what you need to as long as you can you really focus on these capabilities you have to have in place.

Nancy McKinstry

The notion of creating fundamental capabilities, absolutely critical from everything that you've studied, and certainly something that we embrace a hundred percent. But as you think about IWS, right – improve wrap, and sell – what is hardest for most organizations?

Barbara Wixom

It is the ability to come up with information that solves important problems that a market will pay for and that's a key thing that they will pay for, not just appreciate that it's important. Selling requires a lot of different types of managerial levers, you know, in terms of different ways of pricing, of different types of measurement of not just value creation for what money you're bringing in from the solution. But what is the actual cost savings, or how much cost is being made by the customers to understand, for instance, what the top line value is to know what you're even capturing, and such. So there's just so much that goes into it. And I think, initially, when I have a conversation with a leader of an organization, they'll say, we have great data, we're gonna sell it, it's like “Ah, I think there are other ways you can create financial returns that might be a lot more compelling and a lot more straightforward. Let's work your way up to actually getting into selling.”

Nancy McKinstry

Yeah, t's fascinating, because we often find that it goes back to you have to understand the customer value, and of course, the willingness to pay. But if you don't have that distinct “what's in it for the customer” it's very difficult to begin that kind of monetization journey. And so as you did the case studies, what was the case study that you feel most passionately describes all of the key recommendations in your book?

Barbara Wixom

It's hard to choose, I guess. I will say a recent favorite is the case study of BBVA. And the reason I like this case is in 2010-ish, BBVA was a very traditional bank that used data, but in traditional ways. Certainly, they were not an organization you would aspire to in how they are monetizing data. Fast forward to today. This is 130,000 person bank, where every employee knows AI from the teller all the way to the board, they've been trained in AI appropriate to who they are, they have an AI factory that powers all kinds of pervasive use of data analytics for, again, improving wrapping, selling. And there is distributed localized activities, making sure that the right monetizations happening, whether you're in product and you're wrapping, or whether you're in process management, and you're improving, or whether you're in their actual solutions group selling to the market. So that was an exciting year that actually wanted best practices and everything. So that was a fun one.

Nancy McKinstry

So, I think sometimes people think it's always the startup world that maybe can innovate. And maybe they can innovate more quickly. But it shows you that even well-established organizations can go on this journey. And that's what we have found as well.

Barbara Wixom

How do you stay patient? And how do you keep things on track, but yet make change?

Nancy McKinstry

I think that, again, it comes with a grounding on your customers, right? And so, what we have found is you don't want to be so far ahead of your customers that it takes a very long time to get a return for shareholders. But at the other hand, you don't want to be late. So, it's trying to get that balance between how do you invest over time? And I think what's so interesting about data monetization is that there's a lot of small ways that you can create that delight for your customers that eventually turns into monetization. And I think kind of having enough things that are in that more incremental stage that can bring returns early, while you take a couple of bigger bets, that are brand new products. And we have a couple of those in the portfolio that came out maybe five or six years. And it takes a long time for that locomotive to get up to speed. But it's, again, creating that balance between shorter-term opportunities for monetization and longer-term bets that matters. And it, again, goes back to really understanding your customers in the markets.

Erin Aksoy

I also love that you guys talked about, sort of, what is important for attracting talent. But I want to ask you guys a question there. Nancy, you've been a champion of the importance of data in an organization, I think, and have led us through this digital transformation into expert solutions. So you clearly know something about fostering and supporting data talent within an organization. But what advice would you give somebody pursuing a career in data analytics?

Nancy McKinstry

For me, you clearly have to have the technical expertise, right? But having that mentality around, it's all about your customer. And it could be an internal customer. But really partnering with the people in the organization, or partnering directly with customers, is going to make all the difference in the world. Because what most people want in their career is impact, as you talked about, and impact comes from creating products and services that customers are willing to deploy. So it's just being able to, as data scientists, see the relationship between what you do and customer impact is pretty important. And you've got to partner with a lot of the business people, I think, in order to see that, but what is your view Barb?

Barbara Wixom

So, a lot of the students who are in data analytics with whom I engage, I always am encouraging them to… what's your favorite subject, for instance at school, beyond data analytics? Do you like biology? Do you like sociology? Do what do you like, go spend just as much time becoming an expert in that topic, because it's the combination of that subject and the data analytics that's really going to make the magic when you're in a career.

Nancy McKinstry

That's great advice, great advice. And I know that some of the people that we have throughout the organization in the field of data would echo that, that it's really about the connection of knowing the data science part, but in combination with some end user market.

Erin Aksoy

I think that's something that we do particularly well at Wolters Kluwer. We're growing our data expertise, and you know, the people in those areas, but we have such rich subject matter expertise. So the ability to sit right next to the people that are the compliance experts and the legal experts, while also working with the data scientists, it's a really powerful, fascinating conversation. Thank you both. I just want to ask you, what's sort of the one thing that stood out to you in this conversation? Nancy, if we can start with you.

Nancy McKinstry

I think that what has stood out for me is that it can be powerful to take the first step in the journey and recognize that there are foundational elements you have to build in order to get to monetization. And so, as leaders, we have to make sure that we're setting the right expectations about when you will see things materialize. And I think that's key for people to understand that building the foundation is so critical.

Barbara Wixom

For me, I think it's just the trust that has to be in place pervasively to be successful as a leader. And I think of the trust that you've built over the years, how important that must be in order to make sure that there's alignment with your top leaders, with the rest of the organization, to make sure that work is happening in the right way. And then also beyond to your customers. Just think of all of those bonds of trust that are there that you maybe you can't see, they're invisible, but they're so important for keeping the organization together as it's moving forward in all kinds of new directions. It's pretty special to have that.

Nancy McKinstry

Well, again, especially to have you here today, and very much appreciate you sharing your visions and insights with us because we value that greatly. Thank you.

Barbara Wixom

Thank you.

Erin Aksoy

Thank you, Nancy McKinstry, our Wolters Kluwer CEO, and Barbara Wixom, MIT researcher, professor, and author of the book Data is Everybody's Business. We hope you've enjoyed this conversation and thank you guys. To learn more about Barbara's work at MIT, go to MIT Center for Information System Research. We have interesting roles in data science, AI specialties, and technology specialties, please visit our career site.